0-10 KM TEMPERATURE AND HUMIDITY PROFILES RETRIEVAL FROM GROUND-BASED MICROWAVE RADIOMETER

Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 were inputted into the Mono RTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234 GHz to 58.8 GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model's effect by comparing its output with the real measured data and the microwave radiometer's own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1 K and 2.0 K; the water vapor density's RMS error is between 0.2 g/m^3 and 1.93 g/m3; and the relative humidity's RMS error is between 2.5% and 18.6%.

Primary Analysis of Sounding Data from a Multi-channel Parallel Ground-based Microwave Radiometer

The sounding data of a multi-channel parallel ground-based microwave radiometer （MWR） in Fuzhou station in July and August in 2016 were compared with the sounding data of a radiosonde in the same position in the same period. The results showed that the correlations between the two types of temperature or humidity detected by the microwave radiometer and the radiosonde were significant at 0.05 level, indicating that the overall changing trends of temperature or humidity detected by the two devices were similar. The temperature detected by the microwave radiometer and the radiosonde decreased with the increase of height. The difference between the changes in the height of the zero layer detected by the micro- wave radiometer and the radiosonde was not significant, and their trends were basically the same.

基于微波辐射计观测亮温数据集的神经网络训练反演研究

为了提升国产地基微波辐射计反演大气温湿廓线的精度,增强本地部署设备的观测性能,研究实现了地基微波辐射计的神经网络直接样本反演法和观测亮温预处理的神经网络间接样本反演法。将算法应用于武汉华梦科技有限公司研制的HRA002型国产地基微波辐射计,在武汉国家基本气象站开展了与探空以及美国3台MP-3000A微波辐射计的对比观测试验。试验结果显示,HRA002直接样本反演采用改进网络反演水汽密度、相对湿度均方差分别降低约0.94 g·m^(-3)、5%;观测亮温经过预处理后与模拟亮温的相关性提升明显,预处理前后反演的低层温度、水汽密度和相对湿度与探空观测的均方差分别从2.4 K、3.26 g·m^(-3)和18.79%改善为1.58 K、2.18 g·m^(-3)和14.55%,略高于直接样本反演;与3台MP-3000A的反演结果相比,HRA002采用直接样本反演方法的温度廓线总体优于MP-3000A,HRA002采用间接样本反演方法的水汽密度和相对湿度总体上平均偏差占优而均方差稍逊。研究结果表明改进后的直接样本反演法更贴合辐射计硬件性能,反演精度较高;亮温预处理显著提升了间接样本反演精度,在反演精度总体接近的情况下,弥补了直接样本反演法需要长期观测数据的缺陷;综合采用上述两种算法能够提升国产地基微波辐射计本地化、个体化的观测性能,在反演大气参量廓线方面具有可用性。